# Build Market-Ready Data Products

Organizations often lack a structured path from raw data to a compliant, priced offering. This skill coordinates product design, pricing, matching, and trusted delivery.

## Install

```bash
npx skillstore add 图灵数盟/dhturing-data-circulation-trading-team
```

## Metadata

- Status: approved
- Slug: dhturing-data-circulation-trading-team
- Skillstore revision: r1
- Version status: missing
- Tree hash: 6ff783583f34371f3a1f1768a140fc20cb2c62564e3a9938afd8fd1461659ef3
- Author: 图灵数盟
- GitHub username: dhturing
- License: MIT
- Repository: https://github.com/dhturing/turing-data-experts/tree/9c58ffa97ec5b760befef6f910eb23107cdcdbf4/skills/data-circulation-trading-team
- Ref: 50b73f83991cf073cbdf3f5afad2bbac71dd3bae
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: filesystem, external\_commands, network
- Quality score: 77
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dhturing-data-circulation-trading-team
- Manifest: https://skillstore.pages.dev/api/skills/dhturing-data-circulation-trading-team/manifest

## Capabilities

- Assesses data resources, buyer scenarios, and product potential.
- Structures validation gates from product concept through seed-customer testing.
- Develops cost, value, and market-based pricing approaches.
- Plans marketplace entry, buyer matching, negotiation, and transaction documentation.
- Outlines trusted data-space options for sensitive data collaboration.

## Use Cases

- Design a Sellable Data Product: Turn an internal dataset into a validated product concept, buyer profile, and delivery plan.
- Prepare a Defensible Price: Build a pricing range using costs, buyer value, market comparisons, and negotiation limits.
- Plan Sensitive Data Collaboration: Assess trusted data-space models, participation rules, and technical controls for restricted data use.

## Prompt Templates

### Assess Product Potential

```
Assess whether our dataset can become a data product. Identify buyer scenarios, required evidence, key risks, and the next validation step.
```

### Create a Product Plan

```
Create a staged data product plan for [dataset]. Include target buyers, value hypothesis, MVP, seed-customer validation, deliverables, and decision gates.
```

### Develop Pricing and Negotiation

```
Develop pricing for [product]. Compare cost, value, and market methods, then propose anchor, target, and minimum positions with stated assumptions.
```

### Design an End-to-End Transaction

```
Design an end-to-end transaction for [product and jurisdiction]. Cover compliance gates, marketplace route, matching, contract terms, delivery controls, and evidence records.
```

## Limitations

- Does not replace legal, regulatory, security, or accredited valuation advice.
- Cannot guarantee marketplace listing, buyer interest, pricing, or transaction completion.
- Depends on accurate data inventories, buyer evidence, costs, and jurisdiction details.
- Provides planning guidance but does not execute transactions or deploy infrastructure.

## Best Practices

- Validate buyer demand before detailed pricing or marketplace preparation.
- State evidence, assumptions, jurisdiction, and pricing basis in every recommendation.
- Use compliance and security gates before sharing sensitive or regulated data.

## Anti Patterns

- Do not promise a listing, buyer, transaction, or specific sale price.
- Do not price an unvalidated product using only internal cost estimates.
- Do not treat technical access controls as substitutes for governance and contracts.

## Security Audit

- Audited at: 2026-09-27T03:28:51.538\+00:00
- Summary: All 11 static findings are false positives caused by Markdown formatting, documented installation paths, ordinary website links, or Chinese-language text entropy. The reviewed files contain no executable commands, hidden-file operations, encoded payloads, prompt injection, or data-exfiltration behavior.

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